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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted alphabetically
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Biometrics     Hybrid Journal   (Followers: 51)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 17)
Building Simulation     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Current Research in Biostatistics     Open Access   (Followers: 8)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Demographic Research     Open Access   (Followers: 14)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Extremes     Hybrid Journal   (Followers: 2)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
International Statistical Review     Hybrid Journal   (Followers: 12)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 38, SJR: 3.664, CiteScore: 2)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Econometrics     Hybrid Journal   (Followers: 82)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Journal of Forecasting     Hybrid Journal   (Followers: 19)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Journal of Probability and Statistics     Open Access   (Followers: 10)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 34)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Journal of the Korean Statistical Society     Hybrid Journal  
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 36)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 23)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Metrika     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Optimization Letters     Hybrid Journal   (Followers: 2)
Optimization Methods and Software     Hybrid Journal   (Followers: 6)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 16)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Review of Economics and Statistics     Hybrid Journal   (Followers: 138)
Review of Socionetwork Strategies     Hybrid Journal  
Risk Management     Hybrid Journal   (Followers: 17)
Sankhya A     Hybrid Journal   (Followers: 3)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Sequential Analysis: Design Methods and Applications     Hybrid Journal  
Significance     Hybrid Journal   (Followers: 7)
Sociological Methods & Research     Hybrid Journal   (Followers: 40)
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Stata Journal     Full-text available via subscription   (Followers: 8)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 27)
Statistical Modelling     Hybrid Journal   (Followers: 18)
Statistical Papers     Hybrid Journal   (Followers: 4)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Statistics and Economics     Open Access  
Statistics in Medicine     Hybrid Journal   (Followers: 122)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Stochastic Models     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 11)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
The American Statistician     Full-text available via subscription   (Followers: 25)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)

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Review of Socionetwork Strategies
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1867-3236
Published by Springer-Verlag Homepage  [2469 journals]
  • Insta-spiration Sweeping the Nation: The Influence of Instagram on
           Intention to Travel to Yellowstone National Park

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      Abstract: Abstract Instagram is often blamed for disrupting the tourism industry by causing visitation booms to natural areas. This study examines how Instagram photos and captions influence intent to travel to Yellowstone National Park, testing a model based on the theory of planned behavior. Participants (N = 357) were randomly assigned to view an Instagram post with a photo (photo: landscape or wildlife) and a narrative (historical, tourist, or wildlife) in this between-subjects experiment. Results indicate that photo condition and attitude toward traveling were mediated by political ideology, and attitude was negatively correlated with intent to travel, while greater social return, subjective norms, and perceived behavioral control were all positive predictors of intent to travel.
      PubDate: 2022-04-01
       
  • Utility and Risk Evaluation of Synthetic Data by Orthogonal Transformation

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      Abstract: Abstract Releasing synthetic data in statistical disclosure control makes identifying individual records difficult, as synthetic data differ from the original data. We propose a method for generating synthetic data using orthogonal transformation, along with a utility measure for the data thus generated. This method can control the utility of the generated synthetic data in terms of the proposed utility measure. We applied the method to anonymized data, obtained from a national survey of family income and expenditure in Japan, and generated synthetic data for it. Additionally, we evaluated the utility and risk of the data thus generated and compared them with synthetic data generated through other methods. We find that the proposed method can generate synthetic data with a higher utility than other methods by adjustment of the number of adopted eigen value.
      PubDate: 2022-03-26
       
  • Preface of Special Issue on 8th Competition on Legal Information of
           Extraction and Entailment (COLIEE 2021)

    • Free pre-print version: Loading...

      PubDate: 2022-03-14
       
  • Expectation–Maximization (EM) Clustering as a Preprocessing Method
           for Clinical Pathway Mining

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      Abstract: Abstract Hospital information systems (HIS) are service-oriented systems that focus on payment for medical services. Because all HIS coding for diseases and clinical processes are payment-oriented, they may differ from clinicians’ concepts of diseases and processes. HIS in large-scale hospitals in Japan utilize Diagnostic Procedure Combination (DPC) codes, a disease-coding system that focuses on the use of medical resources. Although DPC codes are very precise for diseases requiring surgery, such as cataracts and lung cancer, classification codes for diseases that do not require surgery, such as cerebral infarction, are less precise, with a single category often covering many subtypes with different clinical courses. This paper proposes a preprocessing method that splits DPC codes into subgroups prior to the application of dual clustering-based clinical pathway mining. This method applies expectation–maximization (EM) clustering to the length of patient stay in the hospital using Akaike Information Criteria (AIC) to select the number of clusters. A dual mining method is subsequently applied to the datasets of subgroups and the meanings of subtype clusters are explored using a text mining method. The proposed method was evaluated using datasets from an HIS at Shimane University hospital as preprocessing for clinical pathway mining. The experimental results showed that the proposed method correctly generated subgroups from the more generalized DPC codes and that the clinical pathways identified after this preprocessing capture the characteristics of processes in real clinical settings.
      PubDate: 2022-03-03
      DOI: 10.1007/s12626-021-00100-w
       
  • Data-Augmentation Method for BERT-based Legal Textual Entailment Systems
           in COLIEE Statute Law Task

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      Abstract: Abstract A legal textual entailment task is a task to recognize entailment between a law article and its statements. In the Competition on Legal Information Extraction/Entailment (COLIEE), this task is designed as a task to confirm the entailment of a yes/no answer from the given civil code article(s). Based on the development of deep-learning-based natural language processing tools such as bidirectional encoder representations from transformers (BERT), many participants in the task used such tools, and the best performance system of COLIEE 2020 was a BERT-based system. However, because of the limitation of the size of training data provided by the task organizer, training such tools to adapt to the variability of the questions is difficult. In this paper, we propose a data-augmentation method to make training data using civil code articles for understanding the syntactic structure of the questions and articles for entailment. Our BERT-based ensemble system, which uses this augmentation method, achieves the best performance (accuracy = 0.7037) in Task 4 of COLIEE 2021. We also introduce the results of additional experiments to discuss the characteristics of the proposed method.
      PubDate: 2022-02-28
      DOI: 10.1007/s12626-022-00104-0
       
  • Reconsidering Meaningful Learning in a Bandit Experiment on Weighted
           Voting: Subjects’ Search Behavior

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      Abstract: Abstract This paper clarifies subjects’ search behavior of correct options behind the experimental results shown by Guerci et al. (Theory Decis 83:131–153, 2017). In the experiment, subjects were asked to choose one of two weighted voting games repeatedly and their payoffs are stochastically determined for each of their choice according to a payoff-generating function that was hidden from subjects. The main results are as follows. (1) In the additional sessions conducted for the treatment without any payoff-related feedback information, it was reconfirmed that subjects learned to choose the correct option that generates higher expected payoffs for them and generalized what they had thought introspectively in a binary choice problem to a similar but different one. (2) Feedback information about payoffs given immediately after subjects’ choice often confused their inference on the relationship between nominal voting weights and actual payoffs so that they took the win-stay-lose-shift strategy in some sessions. (3) Immediate payoff-related feedback information sometimes induced subjects to randomly choose the runs of options.
      PubDate: 2022-02-22
      DOI: 10.1007/s12626-022-00106-y
       
  • Applying BERT Embeddings to Predict Legal Textual Entailment

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      Abstract: Textual entailment classification is one of the hardest tasks for the Natural Language Processing community. In particular, working on entailment with legal statutes comes with an increased difficulty, for example in terms of different abstraction levels, terminology and required domain knowledge to solve this task. In course of the COLIEE competition, we develop three approaches to classify entailment. The first approach combines Sentence-BERT embeddings with a graph neural network, while the second approach uses the domain-specific model LEGAL-BERT, further trained on the competition’s retrieval task and fine-tuned for entailment classification. The third approach involves embedding syntactic parse trees with the KERMIT encoder and using them with a BERT model. In this work, we discuss the potential of the latter technique and why of all our submissions, the LEGAL-BERT runs may have outperformed the graph-based approach.
      PubDate: 2022-02-19
      DOI: 10.1007/s12626-022-00101-3
       
  • Overview and Discussion of the Competition on Legal Information
           Extraction/Entailment (COLIEE) 2021

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      Abstract: Abstract We summarize the 8th Competition on Legal Information Extraction and Entailment. In this edition, the competition included five tasks on case law and statute law. The case law component includes an information retrieval Task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law component includes an information retrieval Task (Task 3), an entailment/question answering task based on retrieved civil code statutes (Task 4) and an entailment/question answering task without retrieved civil code statutes (Task 5). Participation was open to any group based on any approach. Eight different teams participated in the case law competition tasks, most of them in more than one task. We received results from six teams for Task 1 (16 runs) and 6 teams for Task 2 (17 runs). On the statute law task, there were eight different teams participating, most in more than one task. Six teams submitted a total of 18 runs for Task 3, 6 teams submitted a total of 18 runs for Task 4, and 4 teams submitted a total of 12 runs for Task 5. Here we summarize the approaches, our official evaluation, and analysis on our data and submission results.
      PubDate: 2022-02-15
      DOI: 10.1007/s12626-022-00105-z
       
  • Legal Information Retrieval and Entailment Based on BM25, Transformer and
           Semantic Thesaurus Methods

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      Abstract: Abstract We describe the techniques applied by the University of Alberta (UA) team in the most recent Competition on Legal Information Extraction and Entailment (COLIEE 2021). We participated in retrieval and entailment tasks for both case law and statute law; we applied a transformer-based approach for the case law entailment task, an information retrieval technique based on BM25 for legal information retrieval, and a natural language inference mechanism using semantic knowledge applied to statute law texts. This competition included 25 teams from 14 countries; our case law entailment approach was ranked no. 4 in Task 2, the BM25 technique for legal information retrieval was ranked no. 3 in Task 3, and the natural language inference technique incorporating semantic information was ranked no. 4 in Task 4. The combination of the latter two techniques on Task 5 was ranked no. 2. We also performed error analysis of our system in Task 4, which provides some insight into current state-of-the-art and research priorities for future directions.
      PubDate: 2022-02-07
      DOI: 10.1007/s12626-022-00103-1
       
  • Transformer-Based Approaches for Legal Text Processing

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      Abstract: Abstract In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition. Automated processing of legal documents is a challenging task because of the characteristics of legal documents as well as the limitation of the amount of data. With our detailed experiments, we found that Transformer-based pretrained language models can perform well with automated legal text-processing problems with appropriate approaches. We describe in detail the processing steps for each task such as problem formulation, data processing and augmentation, pretraining, finetuning. In addition, we introduce to the community two pretrained models that take advantage of parallel translations in legal domain, NFSP and NMSP. In which, NFSP achieves the state-of-the-art result in Task 5 of the competition. Although the paper focuses on technical reporting, the novelty of its approaches can also be an useful reference in automated legal document processing using Transformer-based models.
      PubDate: 2022-01-25
      DOI: 10.1007/s12626-022-00102-2
       
  • Order Trajectory Analysis for Monitoring Clinical Process

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      Abstract: Abstract A hospital information system (HIS) is an information system developed to manage the administrative, financial and clinical aspects of a hospital. Introduced in the 2000’s in large-scale hospitals, HIS has resulted in a dramatic change in the the clinical environment. HIS stores all the histories of clinical activities in a hospital, including electronic patient records, laboratory data, and X-rays, for example. The advantage of HIS is that all the data are input through the network service and can be retrieved from the terminals inside the hospital. Two of the most important roles of HIS is to transfer clinical orders issued by doctors and nurses to other division and to store results of executed orders. Thus, the numbers of issued and executed orders will reflect the clinical activities in large hospitals. This paper proposes a visualization technique, called order trajectory analysis which visualize the temporal sequences of the number of orders. We applied this technique to monitoring clinical workflow: comparison of order trajectories of different divisions confirmed a problem with issuance of orders in one division. We developed the solutions to the problems and used the order trajectories to check whether the problem was solved or not. Transitions of order trajectories show that the proposed technique can be used for monitoring whether the solution is effective or not.
      PubDate: 2021-11-09
      DOI: 10.1007/s12626-021-00096-3
       
  • Preface of Special Issue on Socionetwork Strategies in the Market of Data
           (ISSMD)

    • Free pre-print version: Loading...

      PubDate: 2021-11-01
      DOI: 10.1007/s12626-021-00095-4
       
  • An Empirical Study of Intention to Continue Using of Digital Ride-hailing
           Platforms

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      Abstract: Abstract Ride-hailing platforms are attracting significant attention recently in an effort to change consumers’ consumption habits. Through the integration of diffusion of innovation theory (DIT) and the technology acceptance model (TAM), this study examines factors that influence the intention to continue using ride-hailing services. To counter the potential limitations of TAM and DIT in digital platforms, additional external constructs are considered including trust and subjective norms in a proposed conceptual model. The conceptual model is empirically explored using an online survey of 329 ride-hailing users in Iran as a developing country. Partial least squares structural equation modelling (PLS-SEM) is employed to analyse the collected data. According to results, relative advantage, compatibility, observability, and perceived ease of use contribute positively to individuals’ perceptions of service usefulness. Furthermore, compatibility and observability have significant positive effects on perceived ease of use, whereas complexity has a significant negative impact on perceived ease of use. Additionally, the outcome reveals trust, social norms, and perceived usefulness affect behavioural intention to continue using ride-hailing platforms.
      PubDate: 2021-11-01
      DOI: 10.1007/s12626-021-00098-1
       
  • Social and Active Inclusion of the Elderly in the City Through Affective
           Walkability

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      Abstract: Abstract The development of cities aware of the needs of all citizens is a priority, especially in the case of elderly people. In this context, particular attention should be paid to the analysis of walkability. It has been proved that performing walking activities produces significant benefits both for physical and mental health, above all in elderly subjects. Besides traditional criteria adopted to evaluate walkability, we here propose a novel approach defined affective walkability. Being able to interpret the emotions of elderly walking in the urban environment and interacting with vehicles and other citizens, it is possible to evaluate if and to which extent an environment is perceived safe, comfortable and walkable. One way to obtain quantitative measures of walkability is to assess safety perception relying on physiological signals that can be considered indicators of emotions and mood. The assessment of affective walkability requires the design and performance of rigorous experiments to properly collect data. In this paper, the proposal of an affective walkability is presented and the developed experimental protocols, their performance and preliminary results are illustrated.
      PubDate: 2021-11-01
      DOI: 10.1007/s12626-021-00091-8
       
  • A Simple Numerical Evaluation of the Incentive Contracts for Japan’s
           Defense Equipment

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      Abstract: Abstract The aim of this study is to numerically evaluate the performance of three schemes in incentive contracts used by the Japan Ministry of Defense for its procurement of defense equipment: Two schemes that had previously been used and one scheme that is currently being used. We formalized the transaction between the Ministry of Defense and a supplier as a principal-agent model and compared the effectiveness of providing an incentive for the supplier to exert effort for its cost reduction to the benefit of the general public in Japan. In a simple numerical study, we specified the probability distribution of the amount of cost reduction per unit and fixed the effort level so that we could interpret whether the supplier chooses to exert effort or not on cost reduction. As a result, it was found that changes in the schemes that have been made did not clearly improve the welfare of the general public and that the incentive scheme currently being used is always the best one among those three schemes.
      PubDate: 2021-10-08
      DOI: 10.1007/s12626-021-00090-9
       
  • Determining an Optimal Data Classification Model for Credibility-Based
           Fake News Detection

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      Abstract: Abstract The existence of fake news is a problem challenging today’s social media enabled world. Fake news can be classified using different methods. Predicting and detecting fake news has proven to be a challenge even for machine learning algorithms. This research attempts to investigate nine such machine learning algorithms to understand their performance with Credibility-Based Fake News Detection. This study uses a standard dataset with features relating to the credibility of news publishers. These features are analysed using each of these algorithms. The results of these experiments are analysed using four evaluation methodologies, namely the Receiver Operating Characteristic (ROC) curve, the precision-recall curve, the Lift curve, and numerical metrics. The analysis of these experiments and results reveals varying performance with the use of each of the nine methods. Based upon our selected dataset, the Two-Class Boosted Decision Tree has proven to be best suited for the purpose of Credibility-Based Fake News Detection. Based upon this conclusion, the main contribution of this paper, a deep analysis of the excellent performance of the Two-Class Boosted Decision Tree for Credibility-Based Fake News Detection is finally presented.
      PubDate: 2021-09-28
      DOI: 10.1007/s12626-021-00093-6
       
  • Depression, Anxiety, and Stress During Times of COVID-19: An Analysis of
           Youngsters Studying in Higher Education in India

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      Abstract: Abstract This study addressed the critical problems of depression, anxiety, and stress, which are prevalent among students pursuing higher education. Specifically, this article aimed to study the level of psychological distress due to the COVID-19 pandemic experienced by young people studying in higher education institutions in India. The study also attempted to identify various coping strategies students adopted to overcome this difficult time. Following a descriptive research design, this study used surveys to collect primary data from 235 students in graduate and undergraduate programs in India. The DASS-21 scale was used to check the levels of depression, anxiety, and stress students experienced. Furthermore, a four-point COPE scale was used to identify coping strategies students adopted. The results showed that students experienced high levels of stress and anxiety during the ongoing COVID-19 pandemic. Although depression levels were not alarming, most students were worried about several aspects of their lives and careers. However, because the data were collected from a relatively small sample, the study is likely not generalisable. Furthermore, most of the data were collected online, which has its limitations. This research likely has significant implications for various stakeholders, such as students, parents, institutions, counsellors, and government and non-government bodies, because it may help them take appropriate actions. These research contributions are original and novel, because the COVID-19 pandemic has posed unprecedented challenges and inspired new solutions to the problems of students and society.
      PubDate: 2021-09-27
      DOI: 10.1007/s12626-021-00089-2
       
  • A Novel Item Cluster-Based Collaborative Filtering Recommendation System

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      Abstract: Abstract Recent exponential expansion of users adopting to applications on the mobile internet, like e-commerce and social networks, warrants mining of the huge data collected from users’ past actions, for improving businesses and services. The core step for mining is to cluster the data meaningfully, conforming to the application. Social network data are structured, and graphical presentation reveals that structure. Therefore, graph clustering is an effective way to divulge the underlying structure in the data. For clustering, calculating similarity between a pair of vectors is the first step. The large dimension of the data, which is often noisy and sparse, makes distance measurement hard. In high dimension, most of the conventional distance metrics fail to work, as the data points are distributed over the surface of the high-dimensional hyper-space. The traditional concept of similarity, and nearest-neighbor does not hold. The variance of distance between any pair of points shrinks as the dimension increases. In this work, we investigate the efficacy of various similarity measures and clustering algorithms on high dimensional data. We experimented with a real-world high-dimensional matrix data, the ratings of movies by users. Clustering of movie items depends on a number of factors like movie genre, actors, directors, prominent acclaimed movie or an obscure one, etc. Different similarity measurements and clustering algorithms were experimented. Clustering results were evaluated by matching with known annotations of the movies. Finally, we proposed a novel recommendation algorithm based on item clustering. Its performance was evaluated with different distance metrics and clustering algorithms. Methods elaborated are applicable to other structured data generated in social network applications, or in biological investigations.
      PubDate: 2021-08-12
      DOI: 10.1007/s12626-021-00084-7
       
  • Epistemological Equation for Analysing Uncontrollable States in Complex
           Systems: Quantifying Cyber Risks from the Internet of Things

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      Abstract: Abstract The Internet-of-Things (IoT) triggers data protection questions and new types of cyber risks. Cyber risk regulations for the IoT, however, are still in their infancy. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. At present, there are no self-assessment methods for quantifying IoT cyber risk posture. It is considered that IoT represent a complex system with too many uncontrollable risk states for quantitative risk assessment. To enable quantitative risk assessment of uncontrollable risk states in complex and coupled IoT systems, a new epistemological equation is designed and tested though comparative and empirical analysis. The comparative analysis is conducted on national digital strategies, followed by an empirical analysis of cyber risk assessment approaches. The results from the analysis present the current and a target state for IoT systems, followed by a transformation roadmap, describing how IoT systems can achieve the target state with a new epistemological analysis model. The new epistemological analysis approach enables the assessment of uncontrollable risk states in complex IoT systems—which begin to resemble artificial intelligence—and can be used for a quantitative self-assessment of IoT cyber risk posture.
      PubDate: 2021-07-22
      DOI: 10.1007/s12626-021-00086-5
       
  • Data Combination for Problem-Solving: A Case of an Open Data Exchange
           Platform

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      Abstract: Abstract In recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.
      PubDate: 2021-05-29
      DOI: 10.1007/s12626-021-00083-8
       
 
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